Mistral Small 24B needs ~21.4 GB VRAM. MacBook Pro M2 Pro 32GB has 23.0 GB. With Q4_K_M quantization, expect ~10 tok/s.
Operating mode
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
Fit status
Tight fit
Decode
10.3 tok/s
TTFT
18833 ms
Safe context
27K
Memory
21.4 GB / 23.0 GB
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Shared-memory contention still exists
The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Tight fit | 10.3 tok/s | 10272 ms | 27K |
| Coding | A | Tight fit | 9.6 tok/s | 20245 ms | 27K |
| Agentic Coding | A | Runs with offload (needs ~0.5 GB host RAM) | 9.6 tok/s | 29411 ms | 27K |
| Reasoning | A | Tight fit | 10.3 tok/s | 22257 ms | 27K |
| RAG | A | Runs with offload (needs ~0.5 GB host RAM) | 9.6 tok/s | 36764 ms |
How Mistral Small 24B (24B params) fits at each quantization level on MacBook Pro M2 Pro 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 9.4 GB | Low | A81 |
Q3_K_S | 3 | 11.8 GB | Low | A82 |
NVFP4 | 4 |
Copy-paste commands to run Mistral Small 24B on your machine.
Run
ollama run mistral-smallYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen3-Coder 30B A3B Instruct | 30.5B | A | 19 tok/s | |
| Qwen 3.5 27B | 27B | S | 8.5 tok/s |
Yes, MacBook Pro M2 Pro 32GB can run Mistral Small 24B with a A grade (Tight fit). Expected decode speed: 9.6 tok/s.
Mistral Small 24B (24B parameters) requires approximately 21.4 GB of memory with Q4_K_M quantization.
The recommended quantization for Mistral Small 24B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M2 Pro 32GB, Mistral Small 24B achieves approximately 9.6 tokens per second decode speed with a time-to-first-token of 20245ms using Q4_K_M quantization.
For coding workloads, Mistral Small 24B on MacBook Pro M2 Pro 32GB receives a A grade with 9.6 tok/s and 27K context.
On MacBook Pro M2 Pro 32GB, Mistral Small 24B can safely use up to 27K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/mistral-small-24b-on-m2-pro-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 27K |
13.4 GB |
| Medium |
| A82 |
Q4_K_M | 4 | 14.6 GB | Medium | A82 |
Q5_K_MBest for your GPU | 5 | 17.3 GB | High | A82 |
Q6_K | 6 | 19.7 GB | High | F0 |
Q8_0 | 8 | 25.7 GB | Very High | F0 |
F16 | 16 | 49.2 GB | Maximum | F0 |
| Qwen 3.6 27B | 27B | S | 7 tok/s |
| Qwen3-VL 30B A3B Instruct | 30B | S | 20.1 tok/s |
| Qwen 3.5 35B A3B | 35B | A | 16.6 tok/s |
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Not always. MacBook Pro M2 Pro 32GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.